Mann-Krzisnik Dylan W, Mitsis Georgios D
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:398-401. doi: 10.1109/EMBC44109.2020.9175469.
We propose a framework for studying the electrophysiological correlates of BOLD-fMRI. This framework relies on structured coupled matrix-tensor factorization (sCMTF), a joint multidimensional decomposition which reveals dynamical interactions between LFP/EEG oscillatory features and BOLD-fMRI data. We test whether LFP/EEG-BOLD co-fluctuations and regional hemodynamic response functions can be estimated by sCMTF using whole-brain modelling of restingstate activity. We produce permuted datasets to show that our framework extracts EEG/LFP temporal patterns that correlate significantly with BOLD signal fluctuations. Our framework is also capable of estimating HRFs that accurately embodies simulated hemodynamics, with a word of caution regarding initialization of the sCMTF algorithm.
我们提出了一个用于研究BOLD功能磁共振成像（fMRI）的电生理相关性的框架。该框架依赖于结构化耦合矩阵张量分解（sCMTF），这是一种联合多维分解方法，可揭示局部场电位（LFP）/脑电图（EEG）振荡特征与BOLD-fMRI数据之间的动态相互作用。我们通过对静息态活动进行全脑建模，测试sCMTF是否能够估计LFP/EEG-BOLD共同波动以及区域血流动力学响应函数。我们生成了置换数据集，以表明我们的框架提取了与BOLD信号波动显著相关的EEG/LFP时间模式。我们的框架还能够估计准确体现模拟血流动力学的血流动力学响应函数（HRFs），不过对于sCMTF算法的初始化需要谨慎对待。
